Spaces:
Running
on
Zero
Running
on
Zero
Update
Browse files
app.py
CHANGED
@@ -1,5 +1,3 @@
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import random
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import gradio as gr
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import numpy as np
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import PIL.Image
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@@ -16,16 +14,33 @@ MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 2048
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@spaces.GPU
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def infer(
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prompt: str,
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seed: int,
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randomize_seed: bool,
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width: int = 1024,
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height: int = 1024,
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num_inference_steps: int = 4,
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progress: gr.Progress = gr.Progress(track_tqdm=True), # noqa: ARG001, B008
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) ->
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"""Generate an image from a text prompt using the FLUX.1 [schnell] model.
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Note:
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Args:
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prompt: A text prompt in English used to guide the image generation. Limited to 77 tokens.
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seed: The seed used for deterministic random number generation.
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randomize_seed: If True, a new random seed will be used instead of the one provided.
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width: Width of the generated image in pixels. Defaults to 1024.
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height: Height of the generated image in pixels. Defaults to 1024.
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num_inference_steps: Number of inference steps to perform. A higher value may improve image quality. Defaults to 4.
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progress: (Internal) Used to display progress in the UI; should not be modified by the user.
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Returns:
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A
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"""
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if randomize_seed:
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seed = random.randint(0, MAX_SEED) # noqa: S311
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generator = torch.Generator().manual_seed(seed)
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prompt=prompt,
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width=width,
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height=height,
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@@ -55,11 +67,10 @@ def infer(
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generator=generator,
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guidance_scale=0.0,
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).images[0]
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return image, seed
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def run_example(prompt: str) -> tuple[PIL.Image.Image, int]:
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return infer(prompt, seed=42
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examples = [
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@@ -132,13 +143,17 @@ with gr.Blocks(css=css) as demo:
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examples=examples,
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fn=run_example,
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inputs=prompt,
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outputs=
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)
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prompt.submit(
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fn=infer,
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inputs=[prompt, seed,
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outputs=
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)
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import gradio as gr
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import numpy as np
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import PIL.Image
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MAX_IMAGE_SIZE = 2048
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def get_seed(randomize_seed: bool, seed: int) -> int:
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"""Determine and return the random seed to use for model generation.
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Args:
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randomize_seed (bool): If True, a random seed (an integer in [0, MAX_SEED)) is generated using NumPy's default random number generator. If False, the provided seed argument is returned as-is.
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seed (int): The seed value to use if randomize_seed is False.
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Returns:
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int: The selected seed value. If randomize_seed is True, a randomly generated integer; otherwise, the value of the seed argument.
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Notes:
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- MAX_SEED is the maximum value for a 32-bit integer (np.iinfo(np.int32).max).
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- This function is typically used to ensure reproducibility or to introduce randomness in model generation.
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"""
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rng = np.random.default_rng()
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return int(rng.integers(0, MAX_SEED)) if randomize_seed else seed
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@spaces.GPU
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def infer(
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prompt: str,
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seed: int,
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width: int = 1024,
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height: int = 1024,
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num_inference_steps: int = 4,
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progress: gr.Progress = gr.Progress(track_tqdm=True), # noqa: ARG001, B008
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) -> PIL.Image.Image:
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"""Generate an image from a text prompt using the FLUX.1 [schnell] model.
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Note:
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Args:
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prompt: A text prompt in English used to guide the image generation. Limited to 77 tokens.
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seed: The seed used for deterministic random number generation.
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width: Width of the generated image in pixels. Defaults to 1024.
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height: Height of the generated image in pixels. Defaults to 1024.
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num_inference_steps: Number of inference steps to perform. A higher value may improve image quality. Defaults to 4.
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progress: (Internal) Used to display progress in the UI; should not be modified by the user.
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Returns:
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A PIL.Image.Image object representing the generated image.
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"""
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generator = torch.Generator().manual_seed(seed)
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return pipe(
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prompt=prompt,
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width=width,
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height=height,
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generator=generator,
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guidance_scale=0.0,
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).images[0]
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def run_example(prompt: str) -> tuple[PIL.Image.Image, int]:
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return infer(prompt, seed=42)
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examples = [
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examples=examples,
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fn=run_example,
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inputs=prompt,
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outputs=result,
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)
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prompt.submit(
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fn=get_seed,
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inputs=[randomize_seed, seed],
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outputs=seed,
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).then(
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fn=infer,
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inputs=[prompt, seed, width, height, num_inference_steps],
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outputs=result,
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)
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